We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues: retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods.

中文翻译：

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图像投影网络：OCTA 图像中的 3D 到 2D 图像分割


我们提出了一种图像投影网络（IPN），它是一种新颖的端到端架构，可以在光学相干断层扫描血管造影（OCTA）图像中实现 3D 到 2D 图像分割。我们的主要见解是构建一个投影学习模块（PLM），该模块使用单向池化层同时进行有效的特征选择和降维。通过组合多个 PLM，所提出的网络可以输入 3D OCTA 数据，并输出 2D 分割结果，例如视网膜血管分割。它为视网膜指标的量化提供了一种新的思路：无需视网膜层分割，无需投影图。我们测试了网络在两个关键视网膜图像分割问题上的性能：视网膜血管（RV）分割和中心凹无血管区（FAZ）分割。 316 个 OCTA 体积上的实验结果表明，IPN 是 3D 到 2D 分割网络的有效实现，并且多模态信息和体积信息的使用使 IPN 的性能优于基线方法。